iiinova — where your digital self keeps evolving

Fabian Fröhlich @frohlichlab.com · 13/09/2026

I would expect them to have realised that we‘ve lost control of AI as a technology, not as tool. Reminds me of the passage below from Against the Day (replace silver with AI). We‘ve been on this trajectory for a while I don‘t see us changing course.

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Fabian Fröhlich @frohlichlab.com · 05/08/2026

Data-driven integration calls breast cancer subtype important; supply EGFR levels to the ODE instead and the subtype signal vanishes. Two explanations with same predictive power so the data cannot arbitrate between a molecular and a systems lens. DMMs surface the ambiguity and let us be the judge.

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Fabian Fröhlich @frohlichlab.com · 05/08/2026

Per usual, mechanistic models stay informative where they fail: DMMs systematically underpredict phospho-ERK under MEK inhibition, which could reflect unmodelled crosstalk with AMPK signalling.

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Fabian Fröhlich @frohlichlab.com · 05/08/2026

The unexpected result: across 63 mammary cell lines, most heterogeneity sits at the inputs and outputs rather than in the core machinery. Baseline ERBB2 activation and ERK-to-RSK gain emerge as the major axes, likely set by endocytic, cytoskeletal and calcium programmes.

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Fabian Fröhlich @frohlichlab.com · 05/08/2026

DMMs couple semi-supervised representation learning to an ODE model of EGFR/MAPK signalling, end-to-end. The encoder proposes cell-line-specific parameters; the ODE model tests them against dynamic perturbation data. Representation and mechanism constrain each other rather than sitting side by side.

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Fabian Fröhlich @frohlichlab.com · 04/08/2026

The result we didn't expect: most heterogeneity across the 63 mammary cell lines manifests at the level of inputs and outputs but isn’t generated by core machinery. Tune MAPK model parameters and you'll get a fit, but you may be crediting the cascade for variation that lives beside it.

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Fabian Fröhlich @frohlichlab.com · 04/08/2026

DMMs couple semi-supervised representation learning to an ODE model of EGFR/MAPK signalling, trained end-to-end. The encoder proposes cell-line-specific parametrisations that the ODE must then make work. Representation and mechanism constrain each other rather than sitting side by side.

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Fabian Fröhlich @frohlichlab.com · 10/02/2026

Not all those who wander are lost.

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